allow for division of loss prior to scaling, for gradient accumulation purposes

dalle2_pytorch/train.py

@@ -169,7 +169,14 @@ class DecoderTrainer(nn.Module):
             ema_unet = self.ema_unets[index]
             ema_unet.update()
 
-    def forward(self, x, *, unet_number, **kwargs):
+    def forward(
+        self,
+        x,
+        *,
+        unet_number,
+        divisor = 1,
+        **kwargs
+    ):
         with autocast(enabled = self.amp):
             loss = self.decoder(x, unet_number = unet_number, **kwargs)
-        return self.scale(loss, unet_number = unet_number)
+        return self.scale(loss / divisor, unet_number = unet_number)

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.79',
+  version = '0.0.80',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',